An extension of Brown functor to cospans of spaces

Let $\mathcal{A}$ be a small abelian category. The purpose of this paper is to introduce and study a category $\overline{\mathcal{A}}$ which implicitly appears in construction of some TQFTs where $\overline{\mathcal{A}}$ is determined by $\mathcal{A}$. If $\mathcal{A}$ is the category of abelian groups, then the TQFTs obtained by Dijkgraaf-Witten theory of abelian groups or Turaev-Viro theory of bicommutative Hopf algebras factor through $\overline{\mathcal{A}}$ up to a scaling. In this paper, we go further by giving a sufficient condition for an $\mathcal{A}$-valued Brown functor to extend to a homotopy-theoretic analogue of $\overline{\mathcal{A}}$-valued TQFT for arbitrary $\mathcal{A}$. The results of this paper and our subsequent paper reproduces TQFTs obtained by DW theory and TV theory

Retrospect and Prospect of "East Asian History" in South Korean High Schools

第3回AAWH （アジア世界史学会）報告集 : シンガポール・南洋理工大学2015年5月29～31日Project leader : Professor MOMOKI Shiro Graduate School of Letters, Osaka UniversityTeaching Asian History to Students and Teachers within New Frameworks of Subjects and Curriculums研究者・教員・市民のための新しい歴史学入門（平成26-29年度科学研究費補助金・基盤研究（A）・課題番号26244034)研究代表者 桃木至朗（大阪大学大学院文学研究科教授）JSPS Grant-in-Aid for Scientific Research (Scientific Research A : “ Creating new research guides to history for professional researchers, teachers and citizens ” Project No.26244034 for the fiscal years 2014-2017)Papers presented at the Third AA WH (Asian Association of World Historians) : Singapore: Nanyang Technological University, May 29-31, 201

Genetic Representations for Evolutionary Minimization of Network Coding Resources

We demonstrate how a genetic algorithm solves the problem of minimizing the resources used for network coding, subject to a throughput constraint, in a multicast scenario. A genetic algorithm avoids the computational complexity that makes the problem NP-hard and, for our experiments, greatly improves on sub-optimal solutions of established methods. We compare two different genotype encodings, which tradeoff search space size with fitness landscape, as well as the associated genetic operators. Our finding favors a smaller encoding despite its fewer intermediate solutions and demonstrates the impact of the modularity enforced by genetic operators on the performance of the algorithm.Comment: 10 pages, 3 figures, accepted to the 4th European Workshop on the Application of Nature-Inspired Techniques to Telecommunication Networks and Other Connected Systems (EvoCOMNET 2007

Seismic damage identification of cable-stayed bridge in near-real-time using unsupervised deep neural network

The 20th working conference of the IFIP Working Group 7.5 on Reliability and Optimization of Structural Systems (IFIP 2022) will be held at Kyoto University, Kyoto, Japan, September 19-20, 2022.Prompt damage identification of infrastructure systems is essential for effective post-disaster responses. However, most infrastructure systems have a high level of structural complexity, making damage identification extremely difficult. To overcome the challenge, the authors recently proposed a deep neural network (DNN) based framework for identifying the seismic damage based on the structural response data recorded during an earthquake event (Kim and Song, 2022). The DNN of the proposed framework is constructed by a Variational Autoencoder, one of the self-supervised DNNs capable of constructing a continuous latent space of input data by learning probabilistic characteristics. The DNN model is trained using the covariance matrices of the snapshot of the response data obtained from the undamaged structure. To consider the load-de-pendency, the undamaged state of the structure is represented by the covariance matrix, which is closest to that obtained from the measured seismic response in the latent space. To identify the severity of the structural damage, a structural damage index based on the difference in the covariance matrices is introduced. This paper improves the DNN-based framework to facilitate its applications to complex structural systems such as the Incheon Grand Bridge, a reinforced concrete cable-stayed bridge in South Korea. To generate train, validation, and test datasets, structural analyses are first performed under the ground motions from the PEER-NGA strong motion data-base. The proposed framework is verified with near-real-time simulations using ground motions with various time steps from the test dataset. The example shows that the proposed framework can accurately identify seismic damage of the complex structural system in near-real-time